Instructions to use Mahalingam/DistilBart-Med-Summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahalingam/DistilBart-Med-Summary with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="Mahalingam/DistilBart-Med-Summary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mahalingam/DistilBart-Med-Summary") model = AutoModelForSeq2SeqLM.from_pretrained("Mahalingam/DistilBart-Med-Summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahalingam/DistilBart-Med-Summary: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/Mahalingam/DistilBart-Med-Summary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahalingam/DistilBart-Med-Summary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahalingam/DistilBart-Med-Summary/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- 34f1d2f2735994d56ac7d4c9be232957bb67f36ce5e772dfb34ed900277dcd08
- Size of remote file:
- 1.22 GB
- SHA256:
- 67abec0e716c8fc11e87bb3f759ce59f2b77b7cff98d73a71a9127bf6d2825f1
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